About the Role
The role involves building and optimizing systems that support training, serving, and monitoring machine learning models, with a focus on reliability, scalability, and low-latency inference in production environments.
Responsibilities
- Develop and maintain infrastructure for training and deploying machine learning models
- Design automated pipelines for continuous integration and delivery of ML systems
- Monitor model performance and implement feedback loops for retraining
- Collaborate with research and engineering teams to transition prototypes into production
- Ensure system reliability and low-latency inference for real-time applications
- Optimize resource utilization across cloud-based compute environments
- Implement version control and reproducibility practices for models and data
- Troubleshoot and resolve issues in distributed ML workflows
- Maintain documentation for ML pipelines and deployment processes
- Support A/B testing and model evaluation frameworks
- Integrate security and privacy safeguards into ML operations
- Scale infrastructure to meet growing data and model complexity demands
- Work closely with software engineers to embed models into end-user applications
- Evaluate and adopt new tools and frameworks for ML lifecycle management
- Ensure compliance with data governance and regulatory standards
- Manage model rollback and failover strategies
- Develop metrics dashboards for system and model health monitoring
- Optimize model serving infrastructure for cost and performance
- Contribute to incident response and post-mortem analysis
- Support on-call rotations for critical ML system issues
- Improve testing frameworks for model validation and data quality
- Collaborate on defining service level objectives for ML endpoints
- Streamline deployment processes across multiple environments
- Participate in architectural reviews for new ML-driven features
- Promote best practices in MLOps across the engineering organization
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid work model with flexibility for remote collaboration
Team
Small, agile team focused on rapid development and deployment of machine learning systems
About Us
We are building technology that understands and influences human behavior through voice. Our platform enables emotionally intelligent interactions by combining deep learning with behavioral science. The team values technical rigor, user empathy, and creative problem-solving in equal measure.
Why Join?
You will work on core infrastructure that powers real-time voice analysis and behavior modeling. The role offers ownership of critical systems, direct impact on product evolution, and exposure to cutting-edge challenges in machine learning deployment. We value initiative, precision, and long-term thinking.
Available for qualified candidates
